Prerequisite
Measures of Variability,
Introduction to Simple Linear Regression,
Partitioning Sums of Squares
Learning Objectives
1. Make judgments about the size of the standard error of the estimate from a scatterplot
2. Compute the standard error of the estimate based on errors of prediction
3. Compute the standard error using Pearson’s correlation
4. Estimate the standard error of the estimate based on a sample
Figure 1 shows two regression examples. You can see that in graph A, the points are closer to the line then they are in graph B. Therefore, the predictions in Graph A are more accurate than in Graph B.
Figure 1. Regressions differing in accuracy of prediction.
The standard error of the estimate is a measure of the accuracy of predictions. Recall that the regression line is the line that minimizes the sum of squared deviations of prediction (also called the sum of squares error). The standard error of the estimate is closely related to this quantity and is defined below:
where sest is the standard error of the estimate, Y is an actual score, Y’ is a predicted score, and N is the number of pairs of scores. The numerator is the sum of squared differences between the actual scores and the predicted scores. Assume the data in Table 1 are the data from a population of five XY pairs.
Table 1. Example data.

X

Y

Y’

YY’

(YY’)^{2}


1.00

1.00

1.210

0.210

0.044


2.00

2.00

1.635

0.365

0.133


3.00

1.30

2.060

0.760

0.578


4.00

3.75

2.485

1.265

1.600


5.00

2.25

2.910

0.660

0.436

Sum

15.00

10.30

10.30

0.000

2.791

The last column shows that the sum of the squared errors of prediction is 2.791. Therefore, the standard error of the estimate is
There is a version of the formula for the standard error in terms of Pearson’s correlation:
where r is the population value of Pearson’s correlation and SSY is
For the data in Table 1, my = 10.30, SSY = 4.597 and r = 0.6268. Therefore,
which is the same value computed previously.
Similar formulas are used when the standard error of the estimate is computed from a sample rather than a population. The only difference is that the denominator is N2 rather than N. The reason N2 rather than N1 is used is that two parameters (the slope and the intercept) were estimated in order to estimate the sum of squares. Formulas comparable to the ones for the population are shown below.